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There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity. [ABSTRACT FROM AUTHOR]

With the rapid development of China's electrical industry, the safe operation of electrical facilities is very important for social stability and people's property safety. The failure detection method of conventional electrical equipment is hand detection, which has high experience of the detection person, lacks detection and error detection, and the detection efficiency is low. With the development of artificial intelligence technology, computer-assisted substation inspection is now possible, and substation inspection using an intelligent inspection robot equipped with an infrared device is one of the main substation inspection methods. In this paper, experiments are carried out using several neural network models. For example, if a faster region convolutional neural networks (RCNN) infrared detection model is employed, a good vg16 in the feature region of the extracted image takes into account the quality of the infrared image and the presence of multiple devices. Infrared images can be used to determine the basic features of various electronic devices. In order to detect targets in infrared images of electrical equipment, the fast RCNN target detection algorithm is used, and the overall recognition accuracy reaches 83.1%, and a good application effect is obtained. [ABSTRACT FROM AUTHOR]